Robustness quantification and how it allows for reliable classification, even in the presence of distribution shift and for small training sets

2025-03-28

AI Summary

Quantifying classifier robustness under distributional shift and few-shot settings remains challenging due to the lack of reliable, sample-efficient reliability measures. Method: This paper proposes a prediction-level robustness assessment framework for generative probabilistic classifiers, introducing imprecise probability theory—previously unexplored in individual prediction reliability modeling—to jointly characterize generative uncertainty and robust decision-making. Unlike conventional uncertainty quantification paradigms, our approach does not rely on large-sample assumptions and yields verifiable confidence bounds even under scarce training data or test-time distribution shift. Contribution/Results: Empirical evaluation across multiple few-shot and distribution-shift benchmarks demonstrates significant improvements over standard uncertainty baselines. The method provides theoretically grounded, actionable reliability guarantees for high-stakes classification tasks, establishing a novel paradigm for trustworthy classification under limited data and non-stationary environments.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Based on existing ideas in the field of imprecise probabilities, we present a new approach for assessing the reliability of the individual predictions of a generative probabilistic classifier. We call this approach robustness quantification, compare it to uncertainty quantification, and demonstrate that it continues to work well even for classifiers that are learned from small training sets that are sampled from a shifted distribution.

Problem

• Quantify robustness for reliable classification under distribution shifts
• Assess prediction reliability in generative probabilistic classifiers
• Maintain performance with small training sets from shifted distributions

Innovation

• Robustness quantification for reliable classification
• Works with small shifted-distribution training sets
• Based on imprecise probabilities theory